Unsafe behavior recognition algorithm and application for water personnel
HE Yunze, ZHOU Hui, WU Xinghui, REN Dantong, DING Meiyou, CHENG Liang
1. College of Electrical and Information Engineering, Hunan University, Changsha 410006, China;
2. School of Ocean Engineering, Jiangsu Ocean University, Lianyungang 222005, China;
3. Zhuhai Yunzhou IntelligentTechnology Co., Ltd., Zhuhai 519085, China
Abstract: In order to ensure the life safety of water personnel, a behavior recognition network based on Faster-RCNN+SlowFast improved was proposed for unsafe behavior recognition of water personnel. The main work includes the following points: First, according to the 95 videos collected, the dataset of water area personnel's action behavior is constructed, in which there are 161687 fast channel frames, 5543 key frames and a total of 9173 labeling frames. Secondly, the Faster-RCNN+SlowFast modular design is used to conduct target location and behavior analysis on the dataset of personnel's action behavior in water area. The experimental results show good performance and can identify the location and behavior of personnel in water area. Thirdly, an improved SlowFast network combined with time-domain compression strategy is proposed. The results show that the recognition accuracy of the improved SlowFast network model increases by 5.0%, and the inference speed increases by about 1.14 times. The experiment proves that the improved network model can effectively help to identify the behavior recognition problms of people in water areas.
Keywords: water environment;behavior recognition;water personnel action behavior dataset;improvement of SlowFast
2023, 49(9):104-110  收稿日期: 2022-6-28;收到修改稿日期: 2022-9-2
基金项目:
作者简介: 何赟泽（1983-），男，山西祁县人，教授，主要研究方向为嵌入式人工智能与边缘计算、红外热成像与机器视觉。
参考文献
[1] 杨刚, 张宇姝, 宋震. 人体动作识别与评价——区别、联系及研究进展[J]. 计算机科学与探索, 2022, 16(5): 991-1007.
[2] 杜思远, 李杰, 郑涛, 等. 基于三子样更新的EKF低成本MEMS姿态估计算法[J]. 中国测试, 2018, 44(3): 38-43.
[3] 郭海艳, 程亮, 杨春利, 等. 面向水面无人艇的目标检测与船舶分类系统研究[J]. 中国测试, 2023, 49(6): 114-121.
[4] 黄晴晴, 周风余, 刘美珍. 基于视频的人体动作识别算法综述[J]. 计算机应用研究, 2020, 37(11): 3213-3219.
[5] 乔羽. 基于Mask R-CNN泳池中溺水行为检测系统的设计与实现[D]. 青岛: 青岛大学, 2019.
[6] FEICHTENHOFER C, FAN H, MALIK J, et al. Slowfast networks for video recognition[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019.
[7] NIU J, YANG R, GUAN W, et al. Spatial-temporal graph convolutional networks for action recognition with adjacency matrix generation network[C]//2nd International Conference on Electronics, Communications and Information Technology (CECIT), 2021.
[8] HUYNH T T, HUA C H, TU N A, et al. Learning 3D spatiotemporal gait feature by convolutional network for person identification[J]. Neurocomputing, 2020, 397(3): 192-202.
[9] TAN D, HE C, WANG Y. Action recognition model based on feature interaction[C]//Proceedings of the 11th International Conference on Computer Engineering and Networks, 2021.
[10] ULLAH A, AHMAD J, MUHAMMAD K, et al. Action recognition in video sequences using deep bi-directional LSTM with CNN features[J]. IEEE access, 2017, 6(99): 1155-1166.
[11] 杨清山, 穆太江. 采用蒸馏训练的时空图卷积动作识别融合模型[J]. 中国图象图形学报, 2022, 27(4): 1290-1301.
[12] SIMONYAN K, ZISSERMAN A. Two-stream convolutional networks for action recognition in videos[J]. Advances in neural information processing systems, 2014, 27(1): 568-576.
[13] WU H, SONG C, YUE S, et al. Dynamic video mix-up for cross-domain action recognition[J]. Neurocomputing, 2022, 471(6): 358-368.
[14] GU C, SUN C, ROSS D A, et al. Ava: A video dataset of spatio-temporally localized atomic visual actions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018.
[15] 郭雨青, 曾庆军, 夏楠, 等. 图像增强水下自主机器人目标识别研究[J]. 中国测试, 2021, 47(11): 47-52.
[16] 彭伟康, 陈爱军, 吴东明, 等. 基于改进Faster R-CNN的水准泡缺陷检测方法[J]. 中国测试, 2021, 47(7): 6-12.